1. Problem

Identifying the breed of a dog given an image of a dog.

2. Data

The data we're using is from Kaggle's dog breed identification competition.

https://www.kaggle.com/c/dog-breed-identification/data

3. Evaluation

The evaluation is a file with prediction probabilities for each dog breed of each test image.

https://www.kaggle.com/c/dog-breed-identification/overview/evaluation

4. Features

Some information about the data:

1. We're dealing with images (unstructured data) so it's probably best we use deep learning/transfer learning.
2. There are 120 breeds of dogs (this means there are 120 different classes).
3. There are around 10,000+ images in the training set (these images have labels).
4. There are around 10,000+ images in the test set (these images have no labels, because we'll want to predict them).

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

!unzip "/content/drive/MyDrive/dog-breed-identification.zip"

Streaming output truncated to the last 5000 lines.
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# Import necessary tools
import tensorflow as tf
import tensorflow_hub as hub
print("TF version:", tf.__version__)
print("TF Hub version:", hub.__version__)

TF version: 2.12.0
TF Hub version: 0.13.0

Getting our data ready (turning into Tensors)

Turning our images into Tensors (numerical representations).

#checkout the labels of our data
import pandas as pd
labels_csv = pd.read_csv("/content/labels.csv")
print(labels_csv.describe())
print(labels_csv.head())

                                      id               breed
count                              10222               10222
unique                             10222                 120
top     000bec180eb18c7604dcecc8fe0dba07  scottish_deerhound
freq                                   1                 126
                                 id             breed
0  000bec180eb18c7604dcecc8fe0dba07       boston_bull
1  001513dfcb2ffafc82cccf4d8bbaba97             dingo
2  001cdf01b096e06d78e9e5112d419397          pekinese
3  00214f311d5d2247d5dfe4fe24b2303d          bluetick
4  0021f9ceb3235effd7fcde7f7538ed62  golden_retriever

#how many images are there for each breed
labels_csv["breed"].value_counts()

scottish_deerhound      126
maltese_dog             117
afghan_hound            116
entlebucher             115
bernese_mountain_dog    114
                       ... 
golden_retriever         67
brabancon_griffon        67
komondor                 67
eskimo_dog               66
briard                   66
Name: breed, Length: 120, dtype: int64

labels_csv["breed"].value_counts().plot.bar(figsize=(20,10))

<Axes: >

labels_csv["breed"].value_counts().median()

82.0

#lets view an image
from IPython.display import Image
Image("/content/train/000bec180eb18c7604dcecc8fe0dba07.jpg")

Getting images and their labels

labels_csv.head()

	id	breed
0	000bec180eb18c7604dcecc8fe0dba07	boston_bull
1	001513dfcb2ffafc82cccf4d8bbaba97	dingo
2	001cdf01b096e06d78e9e5112d419397	pekinese
3	00214f311d5d2247d5dfe4fe24b2303d	bluetick
4	0021f9ceb3235effd7fcde7f7538ed62	golden_retriever

# Create pathnames from image ID's
filenames = ["/content/train/" + fname + ".jpg" for fname in labels_csv["id"]]

# Check the first 10
filenames[:10]

['/content/train/000bec180eb18c7604dcecc8fe0dba07.jpg',
 '/content/train/001513dfcb2ffafc82cccf4d8bbaba97.jpg',
 '/content/train/001cdf01b096e06d78e9e5112d419397.jpg',
 '/content/train/00214f311d5d2247d5dfe4fe24b2303d.jpg',
 '/content/train/0021f9ceb3235effd7fcde7f7538ed62.jpg',
 '/content/train/002211c81b498ef88e1b40b9abf84e1d.jpg',
 '/content/train/00290d3e1fdd27226ba27a8ce248ce85.jpg',
 '/content/train/002a283a315af96eaea0e28e7163b21b.jpg',
 '/content/train/003df8b8a8b05244b1d920bb6cf451f9.jpg',
 '/content/train/0042188c895a2f14ef64a918ed9c7b64.jpg']

Image(filenames[9000])

labels_csv["breed"][9000]

'tibetan_mastiff'

Prepare labels

import numpy as np
labels = labels_csv["breed"].to_numpy()
# labels = np.array(labels) # does same thing as above
labels

array(['boston_bull', 'dingo', 'pekinese', ..., 'airedale',
       'miniature_pinscher', 'chesapeake_bay_retriever'], dtype=object)

len(labels)

10222

#Find the unique label values
unique_breeds = np.unique(labels)
unique_breeds

array(['affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale',
       'american_staffordshire_terrier', 'appenzeller',
       'australian_terrier', 'basenji', 'basset', 'beagle',
       'bedlington_terrier', 'bernese_mountain_dog',
       'black-and-tan_coonhound', 'blenheim_spaniel', 'bloodhound',
       'bluetick', 'border_collie', 'border_terrier', 'borzoi',
       'boston_bull', 'bouvier_des_flandres', 'boxer',
       'brabancon_griffon', 'briard', 'brittany_spaniel', 'bull_mastiff',
       'cairn', 'cardigan', 'chesapeake_bay_retriever', 'chihuahua',
       'chow', 'clumber', 'cocker_spaniel', 'collie',
       'curly-coated_retriever', 'dandie_dinmont', 'dhole', 'dingo',
       'doberman', 'english_foxhound', 'english_setter',
       'english_springer', 'entlebucher', 'eskimo_dog',
       'flat-coated_retriever', 'french_bulldog', 'german_shepherd',
       'german_short-haired_pointer', 'giant_schnauzer',
       'golden_retriever', 'gordon_setter', 'great_dane',
       'great_pyrenees', 'greater_swiss_mountain_dog', 'groenendael',
       'ibizan_hound', 'irish_setter', 'irish_terrier',
       'irish_water_spaniel', 'irish_wolfhound', 'italian_greyhound',
       'japanese_spaniel', 'keeshond', 'kelpie', 'kerry_blue_terrier',
       'komondor', 'kuvasz', 'labrador_retriever', 'lakeland_terrier',
       'leonberg', 'lhasa', 'malamute', 'malinois', 'maltese_dog',
       'mexican_hairless', 'miniature_pinscher', 'miniature_poodle',
       'miniature_schnauzer', 'newfoundland', 'norfolk_terrier',
       'norwegian_elkhound', 'norwich_terrier', 'old_english_sheepdog',
       'otterhound', 'papillon', 'pekinese', 'pembroke', 'pomeranian',
       'pug', 'redbone', 'rhodesian_ridgeback', 'rottweiler',
       'saint_bernard', 'saluki', 'samoyed', 'schipperke',
       'scotch_terrier', 'scottish_deerhound', 'sealyham_terrier',
       'shetland_sheepdog', 'shih-tzu', 'siberian_husky', 'silky_terrier',
       'soft-coated_wheaten_terrier', 'staffordshire_bullterrier',
       'standard_poodle', 'standard_schnauzer', 'sussex_spaniel',
       'tibetan_mastiff', 'tibetan_terrier', 'toy_poodle', 'toy_terrier',
       'vizsla', 'walker_hound', 'weimaraner', 'welsh_springer_spaniel',
       'west_highland_white_terrier', 'whippet',
       'wire-haired_fox_terrier', 'yorkshire_terrier'], dtype=object)

# Turn every label into a boolean array
boolean_labels = [label == unique_breeds for label in labels]
boolean_labels[:2]

[array([False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False,  True, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False]),
 array([False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False,  True, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False, False, False,
        False, False, False])]

# Example: Turning boolean array into integers
print(labels[0]) # original label
print(np.where(unique_breeds == labels[0])) # index where label occurs
print(boolean_labels[0].argmax()) # index where label occurs in boolean array
print(boolean_labels[0].astype(int)) # there will be a 1 where the sample label occurs

boston_bull
(array([19]),)
19
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0]

print(labels[2])
print(boolean_labels[2].astype(int))

pekinese
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0]

Creating our own validation set

# Setup X & y variables
X = filenames
y = boolean_labels

# Set number of images to use for experimenting
NUM_IMAGES = 1000 #@param {type:"slider", min:1000, max:10000, step:1000}

# Let's split our data into train and validation sets
from sklearn.model_selection import train_test_split

# Split them into training and validation of total size NUM_IMAGES
X_train, X_val, y_train, y_val = train_test_split(X[:NUM_IMAGES],
                                                  y[:NUM_IMAGES],
                                                  test_size=0.2,
                                                  random_state=42)

len(X_train), len(y_train), len(X_val), len(y_val)

(800, 800, 200, 200)

Preprocessing Images (turning images into Tensors)

1. Take an image filepath as input
2. Use TensorFlow to read the file and save it to a variable, image
3. Turn our image (a jpg) into Tensors
4. Normalize our image (convert color channel values from from 0-255 to 0-1).
5. Resize the image to be a shape of (224, 224)
6. Return the modified image

#image to numpy
from matplotlib.pyplot import imread
image = imread(filenames[42])
image.shape

(257, 350, 3)

image

array([[[ 89, 137,  87],
        [ 76, 124,  74],
        [ 63, 111,  59],
        ...,
        [ 76, 134,  86],
        [ 76, 134,  86],
        [ 76, 134,  86]],

       [[ 72, 119,  73],
        [ 67, 114,  68],
        [ 63, 111,  63],
        ...,
        [ 75, 131,  84],
        [ 74, 132,  84],
        [ 74, 131,  86]],

       [[ 56, 104,  66],
        [ 58, 106,  66],
        [ 64, 112,  72],
        ...,
        [ 71, 127,  82],
        [ 73, 129,  84],
        [ 73, 130,  85]],

       ...,

       [[  2,  11,  10],
        [  5,  14,  13],
        [  6,  15,  14],
        ...,
        [120, 113,  67],
        [126, 118,  72],
        [122, 114,  67]],

       [[  0,   4,   6],
        [  0,   9,   8],
        [  1,  10,   9],
        ...,
        [105,  98,  52],
        [111, 104,  58],
        [111, 103,  56]],

       [[ 18,  16,  37],
        [ 18,  18,  28],
        [ 17,  20,  11],
        ...,
        [101,  92,  53],
        [ 97,  88,  49],
        [120, 111,  72]]], dtype=uint8)

image[:2]

array([[[ 89, 137,  87],
        [ 76, 124,  74],
        [ 63, 111,  59],
        ...,
        [ 76, 134,  86],
        [ 76, 134,  86],
        [ 76, 134,  86]],

       [[ 72, 119,  73],
        [ 67, 114,  68],
        [ 63, 111,  63],
        ...,
        [ 75, 131,  84],
        [ 74, 132,  84],
        [ 74, 131,  86]]], dtype=uint8)

# turn image into a tensor
tf.constant(image)[:2]

<tf.Tensor: shape=(2, 350, 3), dtype=uint8, numpy=
array([[[ 89, 137,  87],
        [ 76, 124,  74],
        [ 63, 111,  59],
        ...,
        [ 76, 134,  86],
        [ 76, 134,  86],
        [ 76, 134,  86]],

       [[ 72, 119,  73],
        [ 67, 114,  68],
        [ 63, 111,  63],
        ...,
        [ 75, 131,  84],
        [ 74, 132,  84],
        [ 74, 131,  86]]], dtype=uint8)>

# Define image size
IMG_SIZE = 224

# Create a function for preprocessing images
def process_image(image_path, img_size=IMG_SIZE):
  """
  Takes an image file path and turns the image into a Tensor.
  """
  # Read in an image file
  image = tf.io.read_file(image_path)
  # Turn the jpeg image into numerical Tensor with 3 colour channels (Red, Green, Blue)
  image = tf.image.decode_jpeg(image, channels=3)
  # Convert the colour channel values from 0-255 to 0-1 values
  image = tf.image.convert_image_dtype(image, tf.float32)
  # Resize the image to our desired value (224, 224)
  image = tf.image.resize(image, size=[IMG_SIZE, IMG_SIZE])

  return image

Turning our data into batches

Why turn our data into batches?

Let's say you're trying to process 10,000+ images in one go... they all might not fit into memory.

So that's why we do about 32 (this is the batch size) images at a time (you can manually adjust the batch size if need be).

In order to use TensorFlow effectively, we need our data in the form of Tensor tuples which look like this: (image, label).

# Create a simple function to return a tuple (image, label)
def get_image_label(image_path, label):
  """
  Takes an image file path name and the assosciated label,
  processes the image and reutrns a typle of (image, label).
  """
  image = process_image(image_path)
  return image, label

# Define the batch size, 32 is a good start
BATCH_SIZE = 32

# Create a function to turn data into batches
def create_data_batches(X, y=None, batch_size=BATCH_SIZE, valid_data=False, test_data=False):
  """
  Creates batches of data out of image (X) and label (y) pairs.
  Shuffles the data if it's training data but doesn't shuffle if it's validation data.
  Also accepts test data as input (no labels).
  """
  # If the data is a test dataset, we probably don't have have labels
  if test_data:
    print("Creating test data batches...")
    data = tf.data.Dataset.from_tensor_slices((tf.constant(X))) # only filepaths (no labels)
    data_batch = data.map(process_image).batch(BATCH_SIZE)
    return data_batch

  # If the data is a valid dataset, we don't need to shuffle it
  elif valid_data:
    print("Creating validation data batches...")
    data = tf.data.Dataset.from_tensor_slices((tf.constant(X), # filepaths
                                               tf.constant(y))) # labels
    data_batch = data.map(get_image_label).batch(BATCH_SIZE)
    return data_batch

  else:
    print("Creating training data batches...")
    # Turn filepaths and labels into Tensors
    data = tf.data.Dataset.from_tensor_slices((tf.constant(X),
                                               tf.constant(y)))
    # Shuffling pathnames and labels before mapping image processor function is faster than shuffling images
    data = data.shuffle(buffer_size=len(X))

    # Create (image, label) tuples (this also turns the iamge path into a preprocessed image)
    data = data.map(get_image_label)

    # Turn the training data into batches
    data_batch = data.batch(BATCH_SIZE)
  return data_batch

# Create training and validation data batches
train_data = create_data_batches(X_train, y_train)
val_data = create_data_batches(X_val, y_val, valid_data=True)

Creating training data batches...
Creating validation data batches...

# Check out the different attributes of our data batches
train_data.element_spec, val_data.element_spec

((TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None),
  TensorSpec(shape=(None, 120), dtype=tf.bool, name=None)),
 (TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None),
  TensorSpec(shape=(None, 120), dtype=tf.bool, name=None)))

Visualizing Data Batches

import matplotlib.pyplot as plt

# Create a function for viewing images in a data batch
def show_25_images(images, labels):
  """
  Displays a plot of 25 images and their labels from a data batch.
  """
  print(len(labels))
  # Setup the figure
  plt.figure(figsize=(10, 10))
  # Loop through 25 (for displaying 25 images)
  for i in range(25):
    # Create subplots (5 rows, 5 columns)
    ax = plt.subplot(5, 5, i+1)
    # Display an image
    plt.imshow(images[i])
    # Add the image label as the title
    plt.title(unique_breeds[labels[i].argmax()])
    # Turn the grid lines off
    plt.axis("off")

# # Now let's visualize the data in a training batch
train_images, train_labels = next(train_data.as_numpy_iterator())
train_images, train_labels

(array([[[[3.07483375e-01, 3.49342644e-01, 2.10765958e-01],
          [3.01939934e-01, 3.56340826e-01, 2.04888448e-01],
          [3.07727009e-01, 3.83908749e-01, 2.23529428e-01],
          ...,
          [2.09091917e-01, 3.36982816e-01, 1.59209251e-01],
          [2.35069066e-01, 3.45037580e-01, 1.86494514e-01],
          [3.16038609e-01, 4.15443122e-01, 2.75808066e-01]],
 
         [[2.54384279e-01, 3.03685427e-01, 1.62508920e-01],
          [3.79006565e-01, 4.40661997e-01, 2.96095490e-01],
          [1.82132065e-01, 2.60782272e-01, 1.07884862e-01],
          ...,
          [1.61592722e-01, 2.80650795e-01, 1.29029706e-01],
          [2.90526360e-01, 4.00424242e-01, 2.50207573e-01],
          [1.16678104e-01, 2.10915595e-01, 8.91454369e-02]],
 
         [[3.63099873e-01, 4.27694321e-01, 2.86411613e-01],
          [2.45574415e-01, 3.13314229e-01, 1.72409117e-01],
          [1.92311719e-01, 2.76100159e-01, 1.34436458e-01],
          ...,
          [2.46755093e-01, 3.44164073e-01, 2.30050415e-01],
          [4.81115967e-01, 5.78524947e-01, 4.61196512e-01],
          [2.37832546e-01, 3.36080700e-01, 2.09963739e-01]],
 
         ...,
 
         [[2.99806416e-01, 3.74001145e-01, 2.64827430e-01],
          [2.26588011e-01, 3.00786227e-01, 1.91605493e-01],
          [2.62520134e-01, 3.37029964e-01, 2.27226019e-01],
          ...,
          [1.52660921e-01, 2.22032622e-01, 1.44216269e-01],
          [2.01054573e-01, 2.95319617e-01, 1.70131683e-01],
          [2.46637285e-01, 3.69652897e-01, 2.02434272e-01]],
 
         [[2.37482741e-01, 2.91194171e-01, 1.93750218e-01],
          [9.47806612e-02, 1.48492083e-01, 5.10110073e-02],
          [1.86829627e-01, 2.40948960e-01, 1.39362857e-01],
          ...,
          [3.72270420e-02, 7.13306218e-02, 8.40015616e-03],
          [4.47339192e-02, 8.87052268e-02, 1.28772510e-02],
          [1.40994474e-01, 1.89487353e-01, 1.04579270e-01]],
 
         [[2.91449636e-01, 3.38508487e-01, 2.44390815e-01],
          [3.83634686e-01, 4.30693537e-01, 3.36575866e-01],
          [3.48392606e-01, 3.98138762e-01, 2.98646450e-01],
          ...,
          [1.60348698e-01, 1.87799677e-01, 1.25054568e-01],
          [6.66534528e-02, 9.41044316e-02, 3.13593298e-02],
          [9.30164158e-02, 1.20467395e-01, 5.77222928e-02]]],
 
 
        [[[3.52126867e-01, 6.69773936e-01, 8.46244574e-01],
          [3.50297630e-01, 6.67944670e-01, 8.44415307e-01],
          [3.47804040e-01, 6.65451109e-01, 8.41921747e-01],
          ...,
          [8.42065156e-01, 9.35867667e-01, 6.00503445e-01],
          [7.68342078e-01, 8.35008740e-01, 5.58363140e-01],
          [7.56020367e-01, 8.21872771e-01, 5.57648957e-01]],
 
         [[3.52030814e-01, 6.69677913e-01, 8.46148551e-01],
          [3.34442943e-01, 6.52090013e-01, 8.28560650e-01],
          [3.41810256e-01, 6.59457326e-01, 8.35927963e-01],
          ...,
          [7.47917414e-01, 8.49654257e-01, 5.09734631e-01],
          [8.81482124e-01, 9.46951628e-01, 6.75512314e-01],
          [8.64737451e-01, 9.21128273e-01, 6.73524439e-01]],
 
         [[3.52030814e-01, 6.69677913e-01, 8.46148551e-01],
          [3.29555362e-01, 6.47202432e-01, 8.23673069e-01],
          [3.42337817e-01, 6.59984887e-01, 8.36455524e-01],
          ...,
          [7.58522689e-01, 8.73485327e-01, 5.36157429e-01],
          [8.89049530e-01, 9.52706635e-01, 6.90427363e-01],
          [7.97691345e-01, 8.44531357e-01, 6.06768668e-01]],
 
         ...,
 
         [[5.26409447e-01, 6.79350615e-01, 8.08762431e-01],
          [5.03193200e-01, 6.56134367e-01, 7.85546184e-01],
          [5.27135432e-01, 6.80076599e-01, 8.09488416e-01],
          ...,
          [5.54709196e-01, 5.23336649e-01, 4.80199367e-01],
          [5.55904925e-01, 5.04924536e-01, 4.69630420e-01],
          [5.79435885e-01, 5.28455496e-01, 4.93161350e-01]],
 
         [[5.25218785e-01, 6.78159952e-01, 8.07571769e-01],
          [4.95345205e-01, 6.48286402e-01, 7.77698219e-01],
          [5.01186669e-01, 6.54127836e-01, 7.83539653e-01],
          ...,
          [5.45456886e-01, 5.14084339e-01, 4.70947057e-01],
          [5.46860099e-01, 4.95879710e-01, 4.60585594e-01],
          [5.46471000e-01, 4.95490640e-01, 4.60196525e-01]],
 
         [[5.07124722e-01, 6.60065889e-01, 7.89477706e-01],
          [4.96918142e-01, 6.49859309e-01, 7.79271126e-01],
          [4.86650854e-01, 6.39592052e-01, 7.69003868e-01],
          ...,
          [5.53003848e-01, 5.21631300e-01, 4.78494048e-01],
          [5.66779971e-01, 5.15799582e-01, 4.80505437e-01],
          [5.52748621e-01, 5.01768231e-01, 4.66474116e-01]]],
 
 
        [[[6.82823777e-01, 7.28604615e-01, 7.46934474e-01],
          [7.02143908e-01, 7.47924745e-01, 7.74097741e-01],
          [7.78024137e-01, 8.28038156e-01, 8.74515176e-01],
          ...,
          [8.19432855e-01, 8.59698951e-01, 7.55977511e-01],
          [8.16525877e-01, 8.86012018e-01, 7.72618413e-01],
          [8.17323744e-01, 8.99877369e-01, 7.81766593e-01]],
 
         [[6.52504921e-01, 7.11415946e-01, 7.38910794e-01],
          [6.77133441e-01, 7.42034316e-01, 7.80661464e-01],
          [8.10607374e-01, 8.81199121e-01, 9.28888202e-01],
          ...,
          [7.00726569e-01, 7.75586545e-01, 6.89443290e-01],
          [7.01041698e-01, 7.88624823e-01, 6.98015094e-01],
          [7.08857834e-01, 7.98241377e-01, 7.06731498e-01]],
 
         [[6.61409914e-01, 7.28076577e-01, 7.64823735e-01],
          [6.66146636e-01, 7.30125964e-01, 7.77403653e-01],
          [8.76371920e-01, 9.47059333e-01, 9.86902356e-01],
          ...,
          [5.60387552e-01, 6.93143129e-01, 6.24008000e-01],
          [5.63989878e-01, 6.69275224e-01, 6.08609974e-01],
          [5.73975921e-01, 6.74702466e-01, 6.16316617e-01]],
 
         ...,
 
         [[2.27980807e-01, 3.53471011e-01, 2.94647485e-01],
          [1.81803167e-01, 3.11214954e-01, 2.44548276e-01],
          [1.82758257e-01, 3.12169999e-01, 2.44873092e-01],
          ...,
          [8.86466280e-02, 1.12806261e-01, 7.32754618e-02],
          [8.11624005e-02, 1.06477633e-01, 9.13866237e-02],
          [7.45098069e-02, 1.09803930e-01, 9.80392247e-02]],
 
         [[1.92960396e-01, 3.18450570e-01, 2.59627044e-01],
          [1.84276670e-01, 3.13688457e-01, 2.47021794e-01],
          [1.74264699e-01, 3.03676486e-01, 2.36379549e-01],
          ...,
          [8.59594047e-02, 1.10119037e-01, 7.05882385e-02],
          [8.11624005e-02, 1.06477633e-01, 9.13866237e-02],
          [7.45098069e-02, 1.09803930e-01, 9.80392247e-02]],
 
         [[1.94266021e-01, 3.19756210e-01, 2.60932684e-01],
          [2.07536206e-01, 3.36947948e-01, 2.70281285e-01],
          [2.00874463e-01, 3.30286205e-01, 2.62989312e-01],
          ...,
          [8.20378363e-02, 1.06197469e-01, 6.66666701e-02],
          [8.11624005e-02, 1.06477633e-01, 9.13866237e-02],
          [7.45098069e-02, 1.09803930e-01, 9.80392247e-02]]],
 
 
        ...,
 
 
        [[[4.01790291e-02, 4.99954373e-02, 3.85250486e-02],
          [1.76049754e-01, 1.99579164e-01, 1.60363480e-01],
          [2.43481502e-01, 2.67010897e-01, 2.22718194e-01],
          ...,
          [1.98350415e-01, 2.21879840e-01, 1.77359596e-01],
          [1.88815460e-01, 2.12344885e-01, 1.65286049e-01],
          [2.01736793e-01, 2.25266203e-01, 1.78207383e-01]],
 
         [[3.81338149e-01, 3.99085253e-01, 3.70939016e-01],
          [2.60525763e-01, 2.81579375e-01, 2.43188947e-01],
          [2.50708282e-01, 2.74237692e-01, 2.29945004e-01],
          ...,
          [2.65802234e-01, 2.89331645e-01, 2.46196747e-01],
          [2.57665038e-01, 2.81194448e-01, 2.36183941e-01],
          [2.67878562e-01, 2.91407973e-01, 2.44481772e-01]],
 
         [[4.00124252e-01, 4.11888957e-01, 3.83930266e-01],
          [2.24711835e-01, 2.38761202e-01, 2.02042565e-01],
          [2.04635933e-01, 2.28165343e-01, 1.81563690e-01],
          ...,
          [3.73609990e-01, 3.97139400e-01, 3.57923716e-01],
          [3.62676054e-01, 3.86205465e-01, 3.46989781e-01],
          [3.60159874e-01, 3.83689284e-01, 3.37138265e-01]],
 
         ...,
 
         [[4.66329008e-02, 3.48681957e-02, 1.64544862e-03],
          [7.68857896e-02, 6.51210845e-02, 2.98269670e-02],
          [9.29192752e-02, 8.11545700e-02, 4.80109230e-02],
          ...,
          [7.07707226e-01, 7.27315128e-01, 7.41294563e-01],
          [7.02173054e-01, 7.21780956e-01, 7.35760391e-01],
          [6.95672452e-01, 7.15280294e-01, 7.34998822e-01]],
 
         [[4.22770530e-02, 3.05123478e-02, 1.98871538e-04],
          [2.89220922e-02, 1.71573870e-02, 3.27109452e-03],
          [2.27575023e-02, 1.09927971e-02, 2.77048722e-03],
          ...,
          [7.51222253e-01, 7.67932713e-01, 7.98106432e-01],
          [7.51941323e-01, 7.68651664e-01, 7.98825443e-01],
          [7.47116983e-01, 7.63827324e-01, 8.01336408e-01]],
 
         [[5.32819331e-02, 4.15172242e-02, 6.69698697e-03],
          [4.52323034e-02, 3.34675983e-02, 6.95272000e-04],
          [5.56787178e-02, 4.39140089e-02, 1.16917538e-02],
          ...,
          [7.64009953e-01, 7.79696226e-01, 8.22150886e-01],
          [7.76953757e-01, 7.92640030e-01, 8.35094631e-01],
          [7.74676681e-01, 7.90362954e-01, 8.36804450e-01]]],
 
 
        [[[3.95743787e-01, 2.03586921e-01, 1.44763380e-01],
          [3.51250440e-01, 1.59093559e-01, 1.04191601e-01],
          [4.08791572e-01, 2.25073531e-01, 1.77174136e-01],
          ...,
          [4.24100161e-02, 5.80962934e-02, 6.98610023e-02],
          [7.03241974e-02, 8.99320394e-02, 7.30552226e-02],
          [2.75090158e-01, 2.94698000e-01, 2.71168590e-01]],
 
         [[3.78491253e-01, 1.74569681e-01, 1.19667716e-01],
          [3.94451082e-01, 2.02294216e-01, 1.47392243e-01],
          [3.60455215e-01, 1.72535047e-01, 1.26736730e-01],
          ...,
          [6.14051856e-02, 7.70914555e-02, 8.88561606e-02],
          [5.57959192e-02, 7.54037574e-02, 5.85269406e-02],
          [2.22232491e-01, 2.41840333e-01, 2.18310922e-01]],
 
         [[3.86197835e-01, 1.82276294e-01, 1.27374321e-01],
          [4.06543434e-01, 2.11732119e-01, 1.57714978e-01],
          [3.81897211e-01, 1.93294227e-01, 1.47837281e-01],
          ...,
          [2.81209592e-02, 4.38072346e-02, 5.55719435e-02],
          [5.33342734e-02, 7.29421228e-02, 5.60652986e-02],
          [2.62171149e-01, 2.81778991e-01, 2.58249581e-01]],
 
         ...,
 
         [[7.54904628e-01, 2.92159528e-01, 1.74774557e-01],
          [6.82712257e-01, 2.34768644e-01, 1.04089551e-01],
          [6.93733633e-01, 2.51541764e-01, 1.18468702e-01],
          ...,
          [2.96380788e-01, 9.67220962e-02, 8.56400281e-02],
          [2.70228118e-01, 8.38141739e-02, 8.59250575e-02],
          [2.93416947e-01, 1.06538914e-01, 1.16929010e-01]],
 
         [[7.58457780e-01, 2.94586807e-01, 1.76939726e-01],
          [6.86213076e-01, 2.37368554e-01, 1.04630440e-01],
          [7.08107471e-01, 2.76734889e-01, 1.40110239e-01],
          ...,
          [3.61420155e-01, 1.50880218e-01, 1.09125942e-01],
          [3.33745807e-01, 1.39696911e-01, 1.03038430e-01],
          [3.35720479e-01, 1.47485152e-01, 1.08339354e-01]],
 
         [[7.55737185e-01, 2.89070457e-01, 1.66853905e-01],
          [6.94518328e-01, 2.45673820e-01, 1.12935707e-01],
          [7.21174657e-01, 2.90117234e-01, 1.53492585e-01],
          ...,
          [3.15755337e-01, 1.09961301e-01, 2.52699964e-02],
          [2.78623343e-01, 9.45112705e-02, 1.42344553e-02],
          [2.82608330e-01, 1.05148844e-01, 2.13824306e-02]]],
 
 
        [[[4.22157735e-01, 6.19557917e-01, 2.65758663e-01],
          [3.01637232e-01, 5.03169835e-01, 1.25918731e-01],
          [2.85997927e-01, 4.92606848e-01, 8.71633068e-02],
          ...,
          [2.89211661e-01, 4.58725333e-01, 1.05686955e-01],
          [3.85460436e-01, 5.36753595e-01, 2.11623430e-01],
          [4.43245858e-01, 5.73646665e-01, 2.78364360e-01]],
 
         [[3.85661095e-01, 5.85661113e-01, 2.21349806e-01],
          [3.16957325e-01, 5.16963959e-01, 1.51047692e-01],
          [2.76938051e-01, 4.77533311e-01, 1.09035417e-01],
          ...,
          [2.69077122e-01, 5.12331307e-01, 1.48453876e-01],
          [3.18817317e-01, 5.43974698e-01, 2.09827721e-01],
          [2.66587257e-01, 4.71504420e-01, 1.69255868e-01]],
 
         [[4.08446640e-01, 6.08340442e-01, 2.45483845e-01],
          [2.14889571e-01, 4.10964519e-01, 6.33874908e-02],
          [2.31177181e-01, 4.24037248e-01, 1.08934537e-01],
          ...,
          [3.34427655e-01, 5.13602734e-01, 1.90315858e-01],
          [2.84694165e-01, 4.44173932e-01, 1.56189591e-01],
          [1.96789369e-01, 3.40389132e-01, 7.47720227e-02]],
 
         ...,
 
         [[2.38544285e-01, 4.17970002e-01, 5.50072268e-02],
          [2.52902687e-01, 4.44788247e-01, 9.68890041e-02],
          [2.55403876e-01, 4.48695809e-01, 1.26841143e-01],
          ...,
          [3.56920153e-01, 5.09158015e-01, 2.86042899e-01],
          [3.50119352e-01, 4.99454111e-01, 2.76554883e-01],
          [3.14978719e-01, 4.52863872e-01, 2.33886227e-01]],
 
         [[1.61891699e-01, 3.48028272e-01, 4.20717485e-02],
          [2.03280061e-01, 3.22285891e-01, 1.00808844e-01],
          [3.25592816e-01, 4.33057666e-01, 2.20041484e-01],
          ...,
          [3.87059391e-01, 4.81087893e-01, 2.91155905e-01],
          [2.80453533e-01, 3.84536803e-01, 1.77382931e-01],
          [2.76558518e-01, 3.98120552e-01, 1.63164809e-01]],
 
         [[1.50620252e-01, 3.40046048e-01, 4.57020812e-02],
          [1.37418926e-01, 2.43082449e-01, 5.04681394e-02],
          [3.46997231e-01, 4.33271736e-01, 2.50192225e-01],
          ...,
          [2.86184251e-01, 3.59022677e-01, 1.80301905e-01],
          [2.65316099e-01, 3.59346390e-01, 1.55555844e-01],
          [3.11669827e-01, 4.33194906e-01, 1.92744613e-01]]]],
       dtype=float32),
 array([[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ...,
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]]))

len(train_images), len(train_labels)

(32, 32)

# # Now let's visualize the data in a training batch
train_images, train_labels = next(train_data.as_numpy_iterator())
show_25_images(train_images, train_labels)

32

# # Now let's visualize our validation set
val_images, val_labels = next(val_data.as_numpy_iterator())
show_25_images(val_images, val_labels)

32

Building a Model

Before we build a model, there are a few things we need to define:

1. The input shape (our images shape, in the form of Tensors) to our model.
2. The output shape (image labels, in the form of Tensors) of our model.
3. The URL of the model we want to use from TensorFlow Hub - https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4

# Setup input shape to the model
INPUT_SHAPE = [None, IMG_SIZE, IMG_SIZE, 3] # batch, height, width, colour channels

# Setup output shape of our model
OUTPUT_SHAPE = len(unique_breeds)

# Setup model URL from TensorFlow Hub
MODEL_URL = "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"

INPUT_SHAPE

[None, 224, 224, 3]

OUTPUT_SHAPE

120

keras

Takes the input shape, output shape and the model we've chosen as parameters. Defines the layers in a Keras model in sequential fashion (do this first, then this, then that). Compiles the model (says it should be evaluated and improved). Builds the model (tells the model the input shape it'll be getting). Returns the model.

# Create a function which builds a Keras model
def create_model(input_shape=INPUT_SHAPE, output_shape=OUTPUT_SHAPE, model_url=MODEL_URL):
  print("Building model with:", MODEL_URL)

  # Setup the model layers
  model = tf.keras.Sequential([
    hub.KerasLayer(MODEL_URL), # Layer 1 (input layer)
    tf.keras.layers.Dense(units=OUTPUT_SHAPE,
                          activation="softmax") # Layer 2 (output layer)
  ])

  # Compile the model
  model.compile(
      loss=tf.keras.losses.CategoricalCrossentropy(),
      optimizer=tf.keras.optimizers.Adam(),
      metrics=["accuracy"]
  )

  # Build the model
  model.build(INPUT_SHAPE)

  return model

model = create_model()
model.summary()

Building model with: https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 keras_layer (KerasLayer)    (None, 1001)              5432713   
                                                                 
 dense (Dense)               (None, 120)               120240    
                                                                 
=================================================================
Total params: 5,552,953
Trainable params: 120,240
Non-trainable params: 5,432,713
_________________________________________________________________

Creating Callbacks

Callbacks are helper functions a model can use during training to do such things as save its progress, check its progress or stop training early if a model stops improving.

We'll create two callbacks, one for TensorBoard which helps track our models progress and another for early stopping which prevents our model from training for too long.

TensorBoard CallBack

1. Load the TensorBoard notebook extension ✅
2. Create a TensorBoard callback which is able to save logs to a directory and pass it to our model's fit() function. ✅
3. Visualize our models training logs with the %tensorboard magic function (we'll do this after model training). https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard

# Load TensorBoard notebook extension
%load_ext tensorboard

import datetime
import os
# Create a function to build a TensorBoard callback
def create_tensorboard_callback():
  # Create a log directory for storing TensorBoard logs
  logdir = os.path.join("drive/My Drive/Dog Vision/logs",
                        # Make it so the logs get tracked whenever we run an experiment
                        datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
  return tf.keras.callbacks.TensorBoard(logdir)

Early Stopping Callback

Early stopping helps stop our model from overfitting by stopping training if a certain evaluation metric stops improving.

https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping

# Create early stopping callback
early_stopping = tf.keras.callbacks.EarlyStopping(monitor="val_accuracy",
                                                  patience=3)

Training a model (on subset of data)

Our first model is only going to train on 1000 images, to make sure everything is working.

NUM_EPOCHS = 100 #@param {type:"slider", min:10, max:100, step:10}

Function to train the model

1. Create a model using create_model()
2. Setup a TensorBoard callback using create_tensorboard_callback()
3. Call the fit() function on our model passing it the training data, validation data, number of epochs to train for (NUM_EPOCHS) and the callbacks we'd like to use
4. Return the model

# Build a function to train and return a trained model
def train_model():
  """
  Trains a given model and returns the trained version.
  """
  # Create a model
  model = create_model()

  # Create new TensorBoard session everytime we train a model
  tensorboard = create_tensorboard_callback()

  # Fit the model to the data passing it the callbacks we created
  model.fit(x=train_data,
            epochs=NUM_EPOCHS,
            validation_data=val_data,
            validation_freq=1,
            callbacks=[tensorboard, early_stopping])
  # Return the fitted model
  return model

# Fit the model to the data
model = train_model()

Building model with: https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4
Epoch 1/100
25/25 [==============================] - 18s 132ms/step - loss: 4.5676 - accuracy: 0.1013 - val_loss: 3.3975 - val_accuracy: 0.2650
Epoch 2/100
25/25 [==============================] - 2s 86ms/step - loss: 1.6242 - accuracy: 0.6913 - val_loss: 2.1939 - val_accuracy: 0.5050
Epoch 3/100
25/25 [==============================] - 2s 90ms/step - loss: 0.5597 - accuracy: 0.9350 - val_loss: 1.6488 - val_accuracy: 0.5950
Epoch 4/100
25/25 [==============================] - 3s 118ms/step - loss: 0.2447 - accuracy: 0.9887 - val_loss: 1.5112 - val_accuracy: 0.6250
Epoch 5/100
25/25 [==============================] - 4s 141ms/step - loss: 0.1447 - accuracy: 0.9962 - val_loss: 1.4089 - val_accuracy: 0.6400
Epoch 6/100
25/25 [==============================] - 2s 87ms/step - loss: 0.1001 - accuracy: 0.9987 - val_loss: 1.3657 - val_accuracy: 0.6350
Epoch 7/100
25/25 [==============================] - 2s 97ms/step - loss: 0.0748 - accuracy: 1.0000 - val_loss: 1.3420 - val_accuracy: 0.6550
Epoch 8/100
25/25 [==============================] - 2s 87ms/step - loss: 0.0591 - accuracy: 1.0000 - val_loss: 1.3217 - val_accuracy: 0.6550
Epoch 9/100
25/25 [==============================] - 3s 102ms/step - loss: 0.0488 - accuracy: 1.0000 - val_loss: 1.3067 - val_accuracy: 0.6550
Epoch 10/100
25/25 [==============================] - 2s 84ms/step - loss: 0.0411 - accuracy: 1.0000 - val_loss: 1.2854 - val_accuracy: 0.6600
Epoch 11/100
25/25 [==============================] - 2s 84ms/step - loss: 0.0353 - accuracy: 1.0000 - val_loss: 1.2787 - val_accuracy: 0.6650
Epoch 12/100
25/25 [==============================] - 2s 84ms/step - loss: 0.0307 - accuracy: 1.0000 - val_loss: 1.2752 - val_accuracy: 0.6700
Epoch 13/100
25/25 [==============================] - 2s 94ms/step - loss: 0.0272 - accuracy: 1.0000 - val_loss: 1.2616 - val_accuracy: 0.6750
Epoch 14/100
25/25 [==============================] - 4s 164ms/step - loss: 0.0243 - accuracy: 1.0000 - val_loss: 1.2553 - val_accuracy: 0.6800
Epoch 15/100
25/25 [==============================] - 2s 90ms/step - loss: 0.0218 - accuracy: 1.0000 - val_loss: 1.2487 - val_accuracy: 0.6800
Epoch 16/100
25/25 [==============================] - 2s 88ms/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 1.2428 - val_accuracy: 0.6850
Epoch 17/100
25/25 [==============================] - 2s 87ms/step - loss: 0.0179 - accuracy: 1.0000 - val_loss: 1.2381 - val_accuracy: 0.6800
Epoch 18/100
25/25 [==============================] - 3s 122ms/step - loss: 0.0164 - accuracy: 1.0000 - val_loss: 1.2347 - val_accuracy: 0.6850
Epoch 19/100
25/25 [==============================] - 2s 92ms/step - loss: 0.0151 - accuracy: 1.0000 - val_loss: 1.2293 - val_accuracy: 0.6900
Epoch 20/100
25/25 [==============================] - 2s 87ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 1.2290 - val_accuracy: 0.6950
Epoch 21/100
25/25 [==============================] - 2s 84ms/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 1.2253 - val_accuracy: 0.6950
Epoch 22/100
25/25 [==============================] - 2s 87ms/step - loss: 0.0121 - accuracy: 1.0000 - val_loss: 1.2223 - val_accuracy: 0.6950
Epoch 23/100
25/25 [==============================] - 4s 158ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 1.2208 - val_accuracy: 0.6950

our model is overfitting because it's performing far better on the training dataset than the validation dataset, what are some ways to prevent model overfitting in deep learning neural networks?

%tensorboard --logdir drive/My\ Drive/Dog\ Vision/logs

Making and evaluating predictions using a trained model

val_data

<_BatchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 120), dtype=tf.bool, name=None))>

# Make predictions on the validation data (not used to train on)
predictions = model.predict(val_data, verbose=1)
predictions

7/7 [==============================] - 27s 77ms/step

array([[3.0691593e-04, 8.3933315e-05, 1.1694538e-02, ..., 1.0713153e-04,
        3.2333999e-05, 3.2193528e-03],
       [1.7746142e-04, 5.0695726e-05, 5.6152339e-03, ..., 1.9194852e-04,
        4.5034659e-04, 1.5915115e-05],
       [5.8107480e-07, 9.2871778e-07, 2.7547259e-07, ..., 1.4337724e-06,
        3.1553327e-06, 1.6792203e-05],
       ...,
       [8.9070227e-06, 3.2581778e-05, 2.5075613e-04, ..., 4.0611758e-05,
        4.2011394e-05, 8.6930371e-04],
       [2.3796374e-03, 6.3059320e-05, 4.4814497e-05, ..., 1.0999754e-04,
        3.2856788e-05, 4.3347809e-03],
       [3.4477445e-05, 2.7254766e-05, 2.5706980e-04, ..., 1.9058512e-03,
        4.5381833e-04, 2.2673566e-05]], dtype=float32)

# First prediction
index = 42
print(predictions[index])
print(f"Max value (probability of prediction): {np.max(predictions[index])}")
print(f"Sum: {np.sum(predictions[index])}")
print(f"Max index: {np.argmax(predictions[index])}")
print(f"Predicted label: {unique_breeds[np.argmax(predictions[index])]}")

[2.15148375e-05 1.78865739e-05 2.65445869e-05 4.11934161e-06
 2.31707352e-03 6.25214961e-05 3.63948784e-05 2.09654987e-04
 7.47253187e-03 4.05150540e-02 2.82483197e-05 1.03383209e-05
 2.36425549e-04 2.14125309e-03 1.10061350e-03 2.68962653e-03
 5.44603154e-06 4.36809380e-04 2.08211786e-05 6.14646124e-05
 3.36903804e-06 1.93848318e-04 3.33091994e-05 1.43221050e-05
 8.10912903e-03 2.12400410e-05 2.05463057e-05 4.34437206e-05
 5.97762082e-05 5.76347011e-05 5.18923334e-05 7.69742182e-05
 3.04424848e-05 2.22028302e-05 2.29474172e-05 2.04176231e-05
 3.81545688e-05 8.21352442e-05 3.29148570e-05 3.57975364e-01
 4.55455418e-04 5.22691880e-06 1.58994878e-03 1.25123752e-05
 1.94367371e-04 5.70667653e-05 1.44962862e-04 1.72191183e-04
 4.12220470e-05 2.03784264e-04 3.76588723e-05 2.22750441e-05
 1.20248595e-04 2.29220721e-03 6.90572051e-06 1.31674373e-04
 1.10631241e-04 1.12651387e-05 6.82613536e-05 7.75283388e-06
 4.57198075e-05 2.39006331e-04 1.77505183e-06 3.45002954e-05
 1.45148861e-05 2.34275809e-04 1.68019775e-04 3.79088015e-04
 4.05595667e-04 1.77776856e-05 1.45103604e-05 9.81801350e-06
 1.80039933e-05 5.24264178e-05 2.02833780e-05 3.55594202e-05
 1.22191515e-04 5.44138784e-05 1.81357191e-05 2.69384444e-04
 3.71714123e-06 2.58400960e-05 7.15807691e-05 1.25128703e-04
 8.62180532e-05 5.53529026e-05 1.01959107e-04 6.85235909e-06
 1.81582873e-05 4.93591302e-04 8.00686175e-05 3.03400134e-06
 1.84956283e-04 8.48155032e-05 2.18224468e-05 1.58724142e-05
 2.47665412e-05 9.78096705e-06 6.39886493e-05 6.23617307e-05
 1.06748033e-04 6.76579975e-06 2.21651917e-05 8.77616912e-06
 1.71059455e-05 1.73013304e-05 7.03429614e-05 1.98471989e-05
 4.87323223e-06 1.16328745e-04 2.07655958e-05 1.20000646e-03
 2.39977700e-04 5.62374592e-01 1.44989259e-04 1.14084723e-04
 3.45085573e-05 1.83543190e-04 1.31638534e-03 2.75957660e-04]
Max value (probability of prediction): 0.5623745918273926
Sum: 1.0
Max index: 113
Predicted label: walker_hound

unique_breeds[113]

'walker_hound'

# Turn prediction probabilities into their respective label (easier to understand)
def get_pred_label(prediction_probabilities):
  """
  Turns an array of prediction probabilities into a label.
  """
  return unique_breeds[np.argmax(prediction_probabilities)]

# Get a predicted label based on an array of prediction probabilities
pred_label = get_pred_label(predictions[81])
pred_label

'dingo'

Now since our validation data is still in a batch dataset, we'll have to unbatchify it to make predictions on the validation images and then compare those predictions to the validation labels (truth labels).

# Create a function to unbatch a batch dataset
def unbatchify(data):
  """
  Takes a batched dataset of (image, label) Tensors and reutrns separate arrays
  of images and labels.
  """
  images = []
  labels = []
  # Loop through unbatched data
  for image, label in data.unbatch().as_numpy_iterator():
    images.append(image)
    labels.append(unique_breeds[np.argmax(label)])
  return images, labels

# Unbatchify the validation data
val_images, val_labels = unbatchify(val_data)
val_images[0], val_labels[0]

(array([[[0.29599646, 0.43284872, 0.3056691 ],
         [0.26635826, 0.32996926, 0.22846507],
         [0.31428418, 0.27701408, 0.22934894],
         ...,
         [0.77614343, 0.82320225, 0.8101595 ],
         [0.81291157, 0.8285351 , 0.8406944 ],
         [0.8209297 , 0.8263737 , 0.8423668 ]],
 
        [[0.2344871 , 0.31603682, 0.19543913],
         [0.3414841 , 0.36560842, 0.27241898],
         [0.45016077, 0.40117094, 0.33964607],
         ...,
         [0.7663987 , 0.8134138 , 0.81350833],
         [0.7304248 , 0.75012016, 0.76590735],
         [0.74518913, 0.76002574, 0.7830809 ]],
 
        [[0.30157745, 0.3082587 , 0.21018331],
         [0.2905954 , 0.27066195, 0.18401104],
         [0.4138316 , 0.36170745, 0.2964005 ],
         ...,
         [0.79871625, 0.8418535 , 0.8606443 ],
         [0.7957738 , 0.82859945, 0.8605655 ],
         [0.75181633, 0.77904975, 0.8155256 ]],
 
        ...,
 
        [[0.9746779 , 0.9878955 , 0.9342279 ],
         [0.99153054, 0.99772066, 0.9427856 ],
         [0.98925114, 0.9792082 , 0.9137934 ],
         ...,
         [0.0987601 , 0.0987601 , 0.0987601 ],
         [0.05703771, 0.05703771, 0.05703771],
         [0.03600177, 0.03600177, 0.03600177]],
 
        [[0.98197854, 0.9820659 , 0.9379411 ],
         [0.9811992 , 0.97015417, 0.9125648 ],
         [0.9722316 , 0.93666023, 0.8697186 ],
         ...,
         [0.09682598, 0.09682598, 0.09682598],
         [0.07196062, 0.07196062, 0.07196062],
         [0.0361607 , 0.0361607 , 0.0361607 ]],
 
        [[0.97279435, 0.9545954 , 0.92389745],
         [0.963602  , 0.93199134, 0.88407487],
         [0.9627158 , 0.91253304, 0.8460338 ],
         ...,
         [0.08394483, 0.08394483, 0.08394483],
         [0.0886985 , 0.0886985 , 0.0886985 ],
         [0.04514172, 0.04514172, 0.04514172]]], dtype=float32),
 'cairn')

1. Takes an array of prediction probabilities, an array of truth labels and an array of images and an integer. ✅
2. Convert the prediction probabilities to a predicted label. ✅
3. Plot the predicted label, its predicted probability, the truth label and the target image on a single plot. ✅

def plot_pred(prediction_probabilities, labels, images, n=1):
  """
  View the prediction, ground truth and image for sample n
  """
  pred_prob, true_label, image = prediction_probabilities[n], labels[n], images[n]

  # Get the pred label
  pred_label = get_pred_label(pred_prob)

  # Plot image & remove ticks
  plt.imshow(image)
  plt.xticks([])
  plt.yticks([])

  # Change the colour of the title depending on if the prediction is right or wrong
  if pred_label == true_label:
    color = "green"
  else:
    color = "red"

  # Change plot title to be predicted, probability of prediction and truth label
  plt.title("{} {:2.0f}% {}".format(pred_label,
                                    np.max(pred_prob)*100,
                                    true_label),
                                    color=color)

plot_pred(prediction_probabilities=predictions,
          labels=val_labels,
          images=val_images,
          n=77)

Top 10 predictions

def plot_pred_conf(prediction_probabilities, labels, n=1):
  """
  Plus the top 10 highest prediction confidences along with the truth label for sample n.
  """
  pred_prob, true_label = prediction_probabilities[n], labels[n]

  # Get the predicted label
  pred_label = get_pred_label(pred_prob)

  # Find the top 10 prediction confidence indexes
  top_10_pred_indexes = pred_prob.argsort()[-10:][::-1]
  # Find the top 10 prediction confidence values
  top_10_pred_values = pred_prob[top_10_pred_indexes]
  # Find the top 10 prediction labels
  top_10_pred_labels = unique_breeds[top_10_pred_indexes]

  # Setup plot
  top_plot = plt.bar(np.arange(len(top_10_pred_labels)),
                     top_10_pred_values,
                     color="grey")
  plt.xticks(np.arange(len(top_10_pred_labels)),
             labels=top_10_pred_labels,
             rotation="vertical")

  # Change color of true label
  if np.isin(true_label, top_10_pred_labels):
    top_plot[np.argmax(top_10_pred_labels == true_label)].set_color("green")
  else:
    pass

plot_pred_conf(prediction_probabilities=predictions,
               labels=val_labels,
               n=9)

# Let's check out a few predictions and their different values
i_multiplier = 20
num_rows = 3
num_cols = 2
num_images = num_rows*num_cols
plt.figure(figsize=(10*num_cols, 5*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_pred(prediction_probabilities=predictions,
            labels=val_labels,
            images=val_images,
            n=i+i_multiplier)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_pred_conf(prediction_probabilities=predictions,
                 labels=val_labels,
                 n=i+i_multiplier)
plt.tight_layout(h_pad=1.0)
plt.show()

# Evaluate the pre-saved model
model.evaluate(val_data)

7/7 [==============================] - 2s 215ms/step - loss: 1.2208 - accuracy: 0.6950

[1.2207871675491333, 0.6949999928474426]

Training a big dog model 🐶 (on the full data)

len(X), len(y)

(10222, 10222)

# Create a data batch with the full data set
full_data = create_data_batches(X, y)

Creating training data batches...

full_data

<_BatchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 120), dtype=tf.bool, name=None))>

# Create a model for full model
full_model = create_model()

Building model with: https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4

# Create full model callbacks
full_model_tensorboard = create_tensorboard_callback()
# No validation set when training on all the data, so we can't monitor validation accuracy
full_model_early_stopping = tf.keras.callbacks.EarlyStopping(monitor="accuracy",
                                                             patience=3)

# Fit the full model to the full data
full_model.fit(x=full_data,
               epochs=NUM_EPOCHS,
               callbacks=[full_model_tensorboard, full_model_early_stopping])

Epoch 1/100
320/320 [==============================] - 34s 84ms/step - loss: 1.3413 - accuracy: 0.6725
Epoch 2/100
320/320 [==============================] - 25s 79ms/step - loss: 0.3994 - accuracy: 0.8799
Epoch 3/100
320/320 [==============================] - 28s 86ms/step - loss: 0.2367 - accuracy: 0.9376
Epoch 4/100
320/320 [==============================] - 27s 83ms/step - loss: 0.1546 - accuracy: 0.9639
Epoch 5/100
320/320 [==============================] - 30s 95ms/step - loss: 0.1067 - accuracy: 0.9788
Epoch 6/100
320/320 [==============================] - 27s 84ms/step - loss: 0.0780 - accuracy: 0.9867
Epoch 7/100
320/320 [==============================] - 27s 84ms/step - loss: 0.0595 - accuracy: 0.9920
Epoch 8/100
320/320 [==============================] - 26s 82ms/step - loss: 0.0457 - accuracy: 0.9943
Epoch 9/100
320/320 [==============================] - 26s 80ms/step - loss: 0.0383 - accuracy: 0.9955
Epoch 10/100
320/320 [==============================] - 27s 84ms/step - loss: 0.0297 - accuracy: 0.9982
Epoch 11/100
320/320 [==============================] - 29s 91ms/step - loss: 0.0255 - accuracy: 0.9977
Epoch 12/100
320/320 [==============================] - 28s 87ms/step - loss: 0.0244 - accuracy: 0.9980
Epoch 13/100
320/320 [==============================] - 27s 85ms/step - loss: 0.0201 - accuracy: 0.9982

<keras.callbacks.History at 0x7ffa9abb9570>

Making predictions on the test dataset

Since our model has been trained on images in the form of Tensor batches, to make predictions on the test data, we'll have to get it into the same format.

Luckily we created create_data_batches() earlier which can take a list of filenames as input and conver them into Tensor batches.

To make predictions on the test data, we'll:

Get the test image filenames. ✅ Convert the filenames into test data batches using create_data_batches() and setting the test_data parameter to True (since the test data doesn't have labels). ✅ Make a predictions array by passing the test batches to the predict() method called on our model.

# Load test image filenames
test_path = "/content/test/"
test_filenames = [test_path + fname for fname in os.listdir(test_path)]
test_filenames[:10]

['/content/test/f6bed54631ef1691d12509f5516ce979.jpg',
 '/content/test/2b546c0f7a5ba74c9d222c149d2134a0.jpg',
 '/content/test/9f128fad88996b200f434ffe0f216f8a.jpg',
 '/content/test/3aa91929bc1ef5552e1e3259b2e7ea50.jpg',
 '/content/test/3a8c5c89e4e720a8d5e4863bd1226774.jpg',
 '/content/test/8e649b689c5f156ac0c0d0a7ebadf965.jpg',
 '/content/test/497aeee206e26363d3e3a8d151ad0332.jpg',
 '/content/test/b3193a53652297e5e8ae969c2061b55d.jpg',
 '/content/test/2ac416c42469d263085206aa28509a2d.jpg',
 '/content/test/bbd433f853eacb0c2d1d62b36121a834.jpg']

len(test_filenames)

10357

# Create test data batch
test_data = create_data_batches(test_filenames, test_data=True)

Creating test data batches...

test_data

<_BatchDataset element_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None)>

Note: Calling predict() on our full model and passing it the test data batch will take a long time to run (about a ~1hr). This is because we have to process ~10,000+ images and get our model to find patterns in those images and generate predictions based on what its learned in the training dataset.

# Make predictions on test data batch using the loaded full model
test_predictions = full_model.predict(test_data, verbose=1)

324/324 [==============================] - 29s 90ms/step

test_predictions[:10]

array([[1.86047885e-06, 3.58563329e-06, 1.76402864e-07, ...,
        1.64487828e-05, 3.35115790e-01, 3.26560839e-05],
       [3.72042507e-07, 3.53058334e-04, 5.40085712e-05, ...,
        4.45102705e-06, 2.11933111e-05, 1.40733523e-08],
       [8.59493099e-09, 4.32418392e-06, 9.82117116e-01, ...,
        1.61677133e-06, 1.06304014e-08, 3.94280164e-08],
       ...,
       [2.69765743e-09, 1.51068408e-10, 1.34296474e-09, ...,
        3.20685600e-12, 7.76269049e-10, 5.76577987e-11],
       [4.21264716e-12, 2.15546159e-09, 1.60832545e-11, ...,
        4.63752398e-10, 6.08286121e-12, 5.29041366e-10],
       [1.76872010e-08, 9.96792376e-01, 6.11027531e-08, ...,
        4.20125752e-06, 3.21042921e-06, 4.22480966e-08]], dtype=float32)

Preparing test dataset predictions for Kaggle

Looking at the Kaggle sample submission, we find that it wants our models prediction probaiblity outputs in a DataFrame with an ID and a column for each different dog breed. https://www.kaggle.com/c/dog-breed-identification/overview/evaluation

To get the data in this format, we'll:

Create a pandas DataFrame with an ID column as well as a column for each dog breed. ✅ Add data to the ID column by extracting the test image ID's from their filepaths. Add data (the prediction probabilites) to each of the dog breed columns. Export the DataFrame as a CSV to submit it to Kaggle.

# Create a pandas DataFrame with empty columns
preds_df = pd.DataFrame(columns=["id"] + list(unique_breeds))
preds_df.head()

	id	affenpinscher	afghan_hound	african_hunting_dog	airedale	american_staffordshire_terrier	appenzeller	australian_terrier	basenji	basset	...	toy_poodle	toy_terrier	vizsla	walker_hound	weimaraner	welsh_springer_spaniel	west_highland_white_terrier	whippet	wire-haired_fox_terrier	yorkshire_terrier

0 rows × 121 columns

# Append test image ID's to predictions DataFrame
test_ids = [os.path.splitext(path)[0] for path in os.listdir(test_path)]
preds_df["id"] = test_ids

preds_df.head()

	id	affenpinscher	afghan_hound	african_hunting_dog	airedale	american_staffordshire_terrier	appenzeller	australian_terrier	basenji	basset	...	toy_poodle	toy_terrier	vizsla	walker_hound	weimaraner	welsh_springer_spaniel	west_highland_white_terrier	whippet	wire-haired_fox_terrier	yorkshire_terrier
0	f6bed54631ef1691d12509f5516ce979	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2b546c0f7a5ba74c9d222c149d2134a0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	9f128fad88996b200f434ffe0f216f8a	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	3aa91929bc1ef5552e1e3259b2e7ea50	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	3a8c5c89e4e720a8d5e4863bd1226774	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 121 columns

# Add the prediction probabilities to each dog breed column
preds_df[list(unique_breeds)] = test_predictions
preds_df.head()

	id	affenpinscher	afghan_hound	african_hunting_dog	airedale	american_staffordshire_terrier	appenzeller	australian_terrier	basenji	basset	...	toy_poodle	toy_terrier	vizsla	walker_hound	weimaraner	welsh_springer_spaniel	west_highland_white_terrier	whippet	wire-haired_fox_terrier	yorkshire_terrier
0	f6bed54631ef1691d12509f5516ce979	1.860479e-06	3.585633e-06	1.764029e-07	6.149982e-05	1.094121e-07	1.349298e-07	8.378111e-06	1.498601e-06	2.336069e-09	...	5.643035e-05	3.727719e-06	1.527020e-06	8.720304e-09	8.566837e-09	1.233772e-05	8.437134e-07	1.644878e-05	3.351158e-01	3.265608e-05
1	2b546c0f7a5ba74c9d222c149d2134a0	3.720425e-07	3.530583e-04	5.400857e-05	9.646160e-08	1.213474e-06	3.282756e-07	3.488667e-08	9.002141e-04	3.593642e-07	...	4.235754e-09	4.974241e-07	1.444571e-07	5.047765e-06	4.545760e-04	8.275511e-08	5.356288e-07	4.451027e-06	2.119331e-05	1.407335e-08
2	9f128fad88996b200f434ffe0f216f8a	8.594931e-09	4.324184e-06	9.821171e-01	1.637139e-07	3.137834e-07	3.462487e-09	2.429447e-07	1.147572e-08	2.806970e-10	...	3.767186e-09	1.717977e-08	8.120876e-10	6.336710e-08	1.208239e-07	1.692081e-09	1.176808e-08	1.616771e-06	1.063040e-08	3.942802e-08
3	3aa91929bc1ef5552e1e3259b2e7ea50	3.724001e-11	9.881821e-01	3.105471e-08	9.979046e-08	2.590402e-11	3.360835e-10	1.171364e-11	3.170581e-09	2.290353e-09	...	2.851536e-07	1.056985e-11	9.502514e-08	1.928899e-07	1.221173e-06	4.657761e-10	2.856725e-10	4.072490e-05	7.382491e-08	1.930012e-08
4	3a8c5c89e4e720a8d5e4863bd1226774	1.696666e-07	1.470362e-09	6.541494e-09	1.283171e-04	1.564643e-06	2.709110e-08	1.320888e-09	3.190511e-06	1.568012e-07	...	1.969769e-04	1.614829e-04	2.283168e-07	3.119850e-08	6.367690e-07	1.880812e-06	9.981725e-05	6.637193e-07	9.540880e-06	2.875662e-06

5 rows × 121 columns

# Save our predictions dataframe to CSV for submission to Kaggle
preds_df.to_csv("drive/My Drive/Dog Vision/full_model_predictions_submission_1_mobilenetV2.csv",
                index=False)

Making predictions on custom images

To make predictions on custom images, we'll:

Get the filepaths of our own images. Turn the filepaths into data batches using create_data_batches(). And since our custom images won't have labels, we set the test_data parameter to True. Pass the custom image data batch to our model's predict() method. Convert the prediction output probabilities to predictions labels. Compare the predicted labels to the custom images.

# Get custom image filepaths
custom_path = "drive/My Drive/Dog Vision/my-dog-photos/"
custom_image_paths = [custom_path + fname for fname in os.listdir(custom_path)]

custom_image_paths

['drive/My Drive/Dog Vision/my-dog-photos/dogq.jpg',
 'drive/My Drive/Dog Vision/my-dog-photos/dog2.jpg']

# Turn custom images into batch datasets
custom_data = create_data_batches(custom_image_paths, test_data=True)
custom_data

Creating test data batches...

<_BatchDataset element_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None)>

# Make predictions on the custom data
custom_preds = full_model.predict(custom_data)

1/1 [==============================] - 1s 1s/step

custom_preds.shape

(2, 120)

# Get custom image prediction labels
custom_pred_labels = [get_pred_label(custom_preds[i]) for i in range(len(custom_preds))]
custom_pred_labels

['eskimo_dog', 'otterhound']

# Get custom images (our unbatchify() function won't work since there aren't labels... maybe we could fix this later)
custom_images = []
# Loop through unbatched data
for image in custom_data.unbatch().as_numpy_iterator():
  custom_images.append(image)

# Check custom image predictions
plt.figure(figsize=(10, 10))
for i, image in enumerate(custom_images):
  plt.subplot(1, 3, i+1)
  plt.xticks([])
  plt.yticks([])
  plt.title(custom_pred_labels[i])
  plt.imshow(image)